Created
March 8, 2017 16:02
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name: "SimpleTriNet" | |
layer { | |
name: "data" | |
type: "Module" | |
top: "anchor" | |
top: "negative" | |
top: "positive" | |
module_param { | |
module: "triplet_layers" | |
type: "MnistData" | |
param_str: "{ 'data': 'data/mnist_train_data.bin', 'labels': 'data/mnist_train_labels.bin', 'batch_size': 64 }" | |
} | |
} | |
########### Anchor | |
layer { | |
name: "conv1" | |
type: "Convolution" | |
bottom: "anchor" | |
top: "conv1" | |
param { | |
name: "conv1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv1_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 20 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2" | |
param { | |
name: "conv2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv2_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 50 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "conv2" | |
top: "pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "ip1" | |
type: "InnerProduct" | |
bottom: "pool2" | |
top: "ip1" | |
param { | |
name: "ip1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip1_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 500 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu1" | |
type: "ReLU" | |
bottom: "ip1" | |
top: "ip1" | |
} | |
layer { | |
name: "ip2" | |
type: "InnerProduct" | |
bottom: "ip1" | |
top: "ip2" | |
param { | |
name: "ip2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip2_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 10 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "feat" | |
type: "InnerProduct" | |
bottom: "ip2" | |
top: "feat" | |
param { | |
name: "feat_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "feat_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
########### Positive | |
layer { | |
name: "conv1_p" | |
type: "Convolution" | |
bottom: "positive" | |
top: "conv1_p" | |
param { | |
name: "conv1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv1_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 20 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool1_p" | |
type: "Pooling" | |
bottom: "conv1_p" | |
top: "pool1_p" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2_p" | |
type: "Convolution" | |
bottom: "pool1_p" | |
top: "conv2_p" | |
param { | |
name: "conv2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv2_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 50 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool2_p" | |
type: "Pooling" | |
bottom: "conv2_p" | |
top: "pool2_p" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "ip1_p" | |
type: "InnerProduct" | |
bottom: "pool2_p" | |
top: "ip1_p" | |
param { | |
name: "ip1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip1_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 500 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu1_p" | |
type: "ReLU" | |
bottom: "ip1_p" | |
top: "ip1_p" | |
} | |
layer { | |
name: "ip2_p" | |
type: "InnerProduct" | |
bottom: "ip1_p" | |
top: "ip2_p" | |
param { | |
name: "ip2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip2_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 10 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "feat_p" | |
type: "InnerProduct" | |
bottom: "ip2_p" | |
top: "feat_p" | |
param { | |
name: "feat_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "feat_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
########### Negative | |
layer { | |
name: "conv1_n" | |
type: "Convolution" | |
bottom: "negative" | |
top: "conv1_n" | |
param { | |
name: "conv1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv1_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 20 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool1_n" | |
type: "Pooling" | |
bottom: "conv1_n" | |
top: "pool1_n" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2_n" | |
type: "Convolution" | |
bottom: "pool1_n" | |
top: "conv2_n" | |
param { | |
name: "conv2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "conv2_b" | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 50 | |
kernel_size: 5 | |
stride: 1 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool2_n" | |
type: "Pooling" | |
bottom: "conv2_n" | |
top: "pool2_n" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "ip1_n" | |
type: "InnerProduct" | |
bottom: "pool2_n" | |
top: "ip1_n" | |
param { | |
name: "ip1_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip1_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 500 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu1_n" | |
type: "ReLU" | |
bottom: "ip1_n" | |
top: "ip1_n" | |
} | |
layer { | |
name: "ip2_n" | |
type: "InnerProduct" | |
bottom: "ip1_n" | |
top: "ip2_n" | |
param { | |
name: "ip2_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "ip2_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 10 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "feat_n" | |
type: "InnerProduct" | |
bottom: "ip2_n" | |
top: "feat_n" | |
param { | |
name: "feat_w" | |
lr_mult: 1 | |
} | |
param { | |
name: "feat_b" | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "loss" | |
type: "Module" | |
bottom: "feat" | |
bottom: "feat_p" | |
bottom: "feat_n" | |
top: "loss" | |
threshold_param { | |
threshold: 0.2 | |
} | |
module_param { | |
module: "triplet_layers" | |
type: "TripletLoss" | |
} | |
} |
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